Diagnosis Predictive Modle for Dense Density Breast Tissue Based on Radiomics

Development of an Artificial Intelligence Precise Diagnosis Predictive Modle for Dense Density Breast Tissue Based on Radiomics

It is a prospective, observational cohort study of patients with dense breast tissue. The study was based on the radiomics and other clinicopathological information of patients to establish the diagnostic system for breast disease by using artificial intelligence.

Study Overview

• Status: Unknown
• Conditions: Breast Diseases; Artificial Intelligence; Predictive Cancer Model; Dense Breast Density

• Study Type: Observational
• Enrollment (Anticipated): 1000

Contacts and Locations

• Study Contact: Name: Yaping Yang, Doctor; Phone Number: 86020-34071145; Email: yangyap2@mail.sysu.edu.cn

Study Locations

• China
  • Guangdong
    • Guangzhou, Guangdong, China, 510120
      • Recruiting
      • Breast tumor center of Sun Yat-sen Memorial Hospital
      • Contact: Yaping yang, Doctor; Phone Number: 0086-020-34071145; Email: yangyap2@mail.sysu.edu.cn
      • Sub-Investigator: Herui Yao, Doctor
      • Sub-Investigator: Chang Gong, Doctor
      • Sub-Investigator: Yunfang Yu, Doctor
      • Sub-Investigator: Yue Hu, Doctor
      • Sub-Investigator: Junwei Li, Doctor

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years to 80 years (ADULT, OLDER_ADULT)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: Female

• Sampling Method: Non-Probability Sample

Study Population

Female patients aged 18-80 were included in our study who were diagnosed and treated at Sun Yat-sen memorial hospital .

Description

Inclusion Criteria:

• Female patients with dense breast density tissue
• Digital breast tomosynthesis was performed
• Breast ultrasound examination was performed
• Pathologic examination of breast lesions was performed

Exclusion Criteria:

• Incomplete clinicopathologic data
• Female patients with fatty glands of the breast

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
the predictive model's area under curve(AUC)	To assess the predictive accuracy of the radiomic-based and clinical-based model by using operating characteristic curve (ROC) analysis and calculating the area under the curves (AUC).	follow up for 2 year for individuals

Collaborators and Investigators

• Sponsor: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

Study record dates

Study Major Dates

• Study Start (ANTICIPATED): September 1, 2020
• Primary Completion (ANTICIPATED): September 1, 2020
• Study Completion (ANTICIPATED): September 1, 2020

Study Registration Dates

• First Submitted: August 21, 2020
• First Submitted That Met QC Criteria: August 31, 2020
• First Posted (ACTUAL): September 2, 2020

Study Record Updates

• Last Update Posted (ACTUAL): September 2, 2020
• Last Update Submitted That Met QC Criteria: August 31, 2020
• Last Verified: August 1, 2020

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Skin Diseases; Breast Diseases
• Other Study ID Numbers: SYSU_AI01

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No